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Cycling levels in cities keep increasing, which is accompanied with more cyclists being involved in serious road crashes. This paper aims to contribute to safer urban cycling by examining risk factors associated with cycling in the four largest Dutch cities, incorporating spatial and temporal variations in bicycle crash risk. For this purpose, the crashes and exposure metrics are analysed on an hourly temporal resolution. The results reveal that utilising an hourly temporal resolution in the exposure metrics and bicycle crash risk gives more detailed results compared to daily averages of these metrics. Moreover, the exposure to cyclists and motorised vehicles both have a significant impact on bicycle crash risk. The results also imply that separating cyclists from high-speed motorised vehicles might be more important than implementing a lower speed limit to curb the increasing severity of crashes. Despite some local differences, the overall results of the risk factors are remarkably similar across the cities, providing increased generalisability and transferability of the study. The findings indicate that concerns about the effects of increasing bicycle use and large flows of motorised vehicles on bicycle crash risk are valid, showing the importance of efforts towards improving bicycle safety in cities. [ABSTRACT FROM AUTHOR]

We argue that many models for multivariate longitudinal and cross-sectional data analysis have a common ancestry. They all are based on the qualitative idea that if we knew the actual state of the world, the relations between the observed quantities would be truly simple. This is shown to lead directly to factor analysis, IRT, state space models, mixture densities, latent Markov chains, MIMIC, LISREL, and various other common models and techniques. We show how our approach provides a convenient framework for looking at these models. The EM algorithm can be used to estimate the unknown parameters. An additional advantage of our approach is that it can incorporate continuous as well as interval, ordinal and categorical variables .

We present the methodology used for estimating forecasts for the number of road traffic fatalities in 2011-2020 in Cambodia based on observed developments in Cambodian road traffic fatalities and motor vehicle ownership in the years 1995-2009. Using the latent risk time series model baseline forecasts for the fatality risk were estimated for the years 2010-2020. These baseline forecasts were then used to obtain estimates for the future number of fatalities based on three scenarios for the future Cambodian growth in motor vehicle ownership: a low, a middle, and a high growth scenario. The middle growth scenario results in an expected death toll of approximately 3, 200 in 2020. In 2010, it was therefore decided in Cambodia to set the target at a 50% reduction of this number or 1, 600 fatalities in 2020. If it is possible to achieve this target by taking additional actions to improve road safety, then a total of 7, 350 lives could be saved in Cambodia over the whole 2011-2020 period., 1. Introduction Road crashes and their consequences are a growing worldwide problem. This has been well documented in a number of WHO reports. The Global Status Report on road safety [...]